Towards integrated 3D reconstruction of whole human brains at subcellular resolution

以亚细胞分辨率对整个人脑进行集成 3D 重建

基本信息

  • 批准号:
    10415091
  • 负责人:
  • 金额:
    $ 168.47万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-08-22 至 2023-05-31
  • 项目状态:
    已结题

项目摘要

Project Summary A detailed understanding of the anatomical and molecular architectures of brain cells and their brain-wide organization is essential for interrogating human brain function and dysfunction. Extensive efforts have been made toward mapping brain cells through various lenses, which have established invaluable databases yielding new insights. However, integrative extraction of the multimodal properties of various cell-types brain-wide within the same brain, crucial to elucidating complex intercellular relationships, remains nearly impossible. We have developed high-throughput, cost-effective technology platforms to create a fully integrated three-dimensional (3D) human brain cell atlas by simultaneously mapping high-dimensional features (e.g., spatial, molecular, morphological, and microenvironment information) of all cells acquired from the same whole brain. The proposed work will establish the most comprehensive 3D human brain map to date, with unprecedented resolution and completeness. We envision that this atlas will facilitate the integration of a broad range of studies and allow the research community to interrogate human brain structure and function at multiple levels. In Aim 1, we will apply a novel technology to transform whole human brain tissue into indestructible hydrogel–tissue hybrids that allow highly multiplexed molecular labeling and subcellular-resolution volume imaging. In Aim 2, we will apply scalable labeling and imaging technologies to map the brain-wide 3D distribution of various cell-type and structural markers at subcellular resolution within the same brain. Our chemical engineering–based approach to this aim will enable cost-effective, lossless 3D labeling of the entire human brain at lower cost as traditional subsampling approaches. True volume labeling and subcellular-resolution imaging will allow us to extract fine morphological and connectivity information from labeled cells and reconstruct the microenvironment of all cells. In Aim 3, we will use a host of rapid and highly automated algorithms to perform unbiased, integrative high- dimensional phenotyping of all cells based on their spatial location, molecular expression, morphology, and microenvironment. In Aim 4, we will perform super-resolution phenotyping of cells in a selected brain region from the same sample used in Aim 3 to map inter-areal axonal connectivity at single-fiber resolution and to characterize chemical synapses. This integrative approach will likely unveil unique cell-types and brain regions, a crucial step toward a better understanding of brain function. The complete 3D dataset will be linked to magnetic resonance and diffusion spectrum images and existing reference atlases to facilitate the integration of a wide breadth of study at multiple levels and to make the data publicly accessible for mining and analysis.
项目摘要 详细了解脑细胞及其全脑的解剖和分子结构 组织对于询问人脑功能和功能障碍至关重要。经过广泛努力, 通过各种镜头绘制脑细胞,这些镜头已经建立了宝贵的数据库, 产生新的见解。然而,各种细胞类型的多峰特性的综合提取 对于阐明复杂的细胞间关系至关重要的同一大脑中的全脑范围内的细胞,仍然几乎 不可能的我们开发了高通量、高性价比的技术平台, 通过同时映射高维人脑细胞图谱 特征(例如,空间、分子、形态学和微环境信息) 来自同一个大脑这项拟议中的工作将建立最全面的3D人脑地图 迄今为止,以前所未有的决心和完整性。我们设想,这本地图集将有助于 整合广泛的研究,并允许研究界询问人脑 在多个层次上的结构和功能。 在目标1中,我们将应用一种新技术将整个人脑组织转化为坚不可摧的 允许高度多重分子标记和亚细胞分辨率体积的水凝胶-组织杂合体 显像在Aim 2中,我们将应用可扩展的标记和成像技术来绘制全脑3D 不同细胞类型和结构标记物在同一脑内以亚细胞分辨率的分布。我们 化学工程为基础的方法,这一目标将使成本效益,无损的3D标记的 整个人类的大脑以较低的成本作为传统的子采样方法。真实体积标记和 亚细胞分辨率成像将使我们能够从其中提取精细的形态和连通性信息 标记细胞并重建所有细胞的微环境。 在目标3中,我们将使用一系列快速和高度自动化的算法来执行无偏的,综合的高性能, 基于它们的空间位置、分子表达、形态学和生物学特性,对所有细胞进行三维表型分析。 微环境在目标4中,我们将对选定的大脑区域中的细胞进行超分辨率表型分析 从Aim 3中使用的同一样本中,以单纤维分辨率绘制区域间轴突连接, 表征化学突触。这种综合方法可能会揭示独特的细胞类型和大脑 这是更好地了解大脑功能的关键一步。完整的3D数据集将 与磁共振和扩散光谱图像以及现有参考图谱相关联, 在多个层面上整合广泛的研究,并使数据公开可供挖掘 和分析

项目成果

期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Characterizing eating disorder psychopathology and body image related constructs in treatment-seeking Black individuals with binge-eating spectrum disorders.
描述患有暴食谱系障碍的寻求治疗的黑人个体中饮食失调的精神病理学和身体形象相关的结构。
  • DOI:
    10.1007/s40519-021-01165-w
  • 发表时间:
    2022-03
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Lin M;Gillikin LM;Patarinski AGG;Srivastava P;Juarascio AS
  • 通讯作者:
    Juarascio AS
Basic principles of hydrogel-based tissue transformation technologies and their applications.
  • DOI:
    10.1016/j.cell.2021.07.009
  • 发表时间:
    2021-08-05
  • 期刊:
  • 影响因子:
    64.5
  • 作者:
    Choi SW;Guan W;Chung K
  • 通讯作者:
    Chung K
Elasticizing tissues for reversible shape transformation and accelerated molecular labeling.
  • DOI:
    10.1038/s41592-020-0823-y
  • 发表时间:
    2020-06
  • 期刊:
  • 影响因子:
    48
  • 作者:
    Ku T;Guan W;Evans NB;Sohn CH;Albanese A;Kim JG;Frosch MP;Chung K
  • 通讯作者:
    Chung K
Axon Tracing and Centerline Detection using Topologically-Aware 3D U-Nets.
使用拓扑感知 3D U-Net 进行轴突追踪和中心线检测。
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Kwanghun Chung其他文献

Kwanghun Chung的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Kwanghun Chung', 18)}}的其他基金

Mapping the vulnerable locus coeruleus pathways in aging and AD
绘制衰老和 AD 中的脆弱蓝斑通路
  • 批准号:
    10440881
  • 财政年份:
    2022
  • 资助金额:
    $ 168.47万
  • 项目类别:
Mapping the vulnerable locus coeruleus pathways in aging and AD
绘制衰老和 AD 中的脆弱蓝斑通路
  • 批准号:
    10683074
  • 财政年份:
    2022
  • 资助金额:
    $ 168.47万
  • 项目类别:
Platform technologies for scalable highly multiplexed proteomic phenotyping of the brain
用于可扩展的高度多重大脑蛋白质组表型分析的平台技术
  • 批准号:
    10369777
  • 财政年份:
    2021
  • 资助金额:
    $ 168.47万
  • 项目类别:
Towards integrated 3D reconstruction of whole human brains at subcellular resolution
以亚细胞分辨率对整个人脑进行集成 3D 重建
  • 批准号:
    9584926
  • 财政年份:
    2018
  • 资助金额:
    $ 168.47万
  • 项目类别:
Towards integrated 3D reconstruction of whole human brains at subcellular resolution
以亚细胞分辨率对整个人脑进行集成 3D 重建
  • 批准号:
    9768578
  • 财政年份:
    2018
  • 资助金额:
    $ 168.47万
  • 项目类别:
Proteome-Driven Holistic Reconstruction of Organ-Wide Multi-Scale Networks
蛋白质组驱动的全器官多尺度网络的整体重建
  • 批准号:
    9982025
  • 财政年份:
    2016
  • 资助金额:
    $ 168.47万
  • 项目类别:

相似海外基金

DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 168.47万
  • 项目类别:
    Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 168.47万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 168.47万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 168.47万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 168.47万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 168.47万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 168.47万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 168.47万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 168.47万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 168.47万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了